Construction of a predictive model for lower respiratory tract infection in children with leukemia after chimeric antigen receptor T cell therapy

Introduction

Background

Chimeric antigen receptor T (CAR-T) cells have achieved breakthrough results in treating refractory/relapsed leukemia in children (1,2). As research keeps developing and clinical application increases, infection events after CAR-T cell therapy have gradually drawn researchers’ attention. Infection is one of the common adverse events after CAR-T cell therapy, among which the incidence of infection within 0–30 days after CAR-T cell infusion is the highest, ranging from 21.7% to 41.5% (3-7). Following CAR-T cell therapy, patients develop a wide range of infections, including infections targeting different organs and invasive infections like bacteremia. In Park’s study, lower respiratory tract infection (LRTI) events, including pneumonia, pulmonary aspergillosis, and pulmonary mucormycosis, accounted for 19.2% of the total number of infection events and resulted in patient death (3).

LRTIs are a general term for infections that occur in the trachea, bronchi, bronchioles, and lung tissues. The respiratory tract is an organ that communicates with the outside world. It is exposed to various microorganisms. When its own defense mechanisms fail, it can easily be attacked by pathogenic bacteria and get infected.

A 2019 study showed that morbidity and mortality from LRTIs remained a significant burden of disease worldwide (8). In particular, LRTIs are an important cause of infectious events and mortality in children undergoing chemotherapy, hematopoietic stem cell transplantation, and CAR-T cell therapy for malignant diseases, as these children are generally immunocompromised (9). Therefore, the prevention of LRTI in CAR-T cell therapy is a common concern for researchers and medical staff.

The identification of patients at high risk for LRTI is the basis for researchers and medical staff to conduct prospective and targeted interventions. However, at present, there are few studies on the risk factors and predictive models of LRTI in children with leukemia undergoing CAR-T cell therapy.

Objective

This study aims to establish a predictive model for the risk of LRTI based on the highly available clinical variables, which is helpful for early identification of children with a high risk of LRTI after CAR-T therapy and provides a basis for accurate management in the future. This study does not include internal and external validation of the predictive model. We present this article in accordance with the TRIPOD reporting checklist (available at https://tp.amegroups.com/article/view/10.21037/tp-2025-503/rc).

Methods

Study setting

This study was conducted in a tertiary A children’s hospital in Shanghai, China, from November 2023 to December 2024. This study was conducted in accordance with the Declaration of Helsinki and its subsequent amendments. This study was approved by the Ethics Committee of Shanghai Children’s Medical Center Affiliated to Shanghai Jiao Tong University (No. SCMCIRB-K202318-1). The researchers provided detailed explanations to the parents or guardians of all eligible children regarding the study’s purpose, procedures, potential benefits, and risks. It was explicitly stated that they had the right to withdraw from the study at any time without any impact on the child’s routine care. After ensuring their full understanding, written informed consent was obtained from the voluntary participating guardians.

Study design and population

This study was a retrospective study. Children with leukemia who received CAR-T cell therapy were included. They have been identified as relapsed or refractory leukemia, and have performed tumor cell target screening by flow cytometry to find one or more targets that can be used for CAR-T cell therapy. Inclusion criteria: (I) leukemia children planned to receive CAR-T cell therapy; (II) age ≤18 years; (III) children/family members sign informed consent form and agree to participate in the investigation. Exclusion criteria: (I) children with pre-existing LRTI at the time of CAR-T cell infusion; (II) diagnosed with a mental state that prevents participation in the study. Logistic multivariate analysis of this study was expected to include 5–10 predictive factors. In combination with literature data (3-7) and hospital investigation, which showed the incidence of LRTI within 0–30 days after CAR-T cell therapy peaked at 30% in July 2023 in Shanghai Children’s Medical Center. Therefore, the incidence rate of LRTI after CAR-T cell treatment was estimated at 30% in this study. The sample size required to establish a predictive model was at least 167–334 cases. Considering that the data for some samples may be incomplete, we increased the sample size by 20%. Therefore, we need at least 200–400 samples.

Data collection tool and procedure

The following information was obtained from a demographic questionnaire, including gender, age, weight, height, diagnosis, and score of the Screening Tool for the Assessment of Malnutrition in Pediatrics (STAMP).

Disease-related data includes the frequency of relapses, the frequency of previous hematopoietic stem cell transplants, the frequency of previous CAR-T treatments, minimal residual disease (MRD), the history of infection before admission, including upper respiratory tract infection (URTI), LRTI, and other infections, and antibiotics used before admission. The data were collected from hospital records.

CAR-T cell therapy-related data includes dosage of CAR-T, source of CAR-T (autologous/allogeneic), target of CAR-T, whether received more chemotherapy than fludarabine and cyclophosphamide, radiotherapy before CAR-T cell infusion, surgery before CAR-T cell infusion, hospital stay before CAR-T infusion, and ICU stay before CAR-T infusion. The data were collected from hospital records.

Clinical laboratory indicators include white blood cell count (WBC), absolute neutrophil count (ANC), hemoglobin (HGB), platelet count (PLT), C-reactive protein (CRP), interleukin-6 (IL-6), lymphocyte count (LYM), immunoglobulin G (IgG), immunoglobulin M (IgM), immunoglobulin A (IgA), albumin (ALB), etc. These indicators are measured on the day of CAR-T cell infusion. The data were collected from hospital records.

Adverse event information includes occurrence and grades of cytokine release syndrome (CRS) according to ASTCT criteria (10), occurrence and grades of immune effector cell-associated neurotoxicity syndrome (ICANS) according to ASTCT criteria (10), dose of tocilizumab, dose of dexamethasone, dose of methylprednisolone, occurrence of mucosal damage, intubation, occurrence of other infections as URTIs, intestinal tract infections, bloodstream infections (BSIs), local infections, central nervous system (CNS) infections. The data were collected from hospital records.

In Shanghai Children’s Medical Center, current antimicrobial prophylaxis guidelines have been followed at the initiation of CAR-T cell therapy. The antimicrobial prophylaxis guidelines in Shanghai Children’s Medical Center, including: (I) clinicians select antibiotics based on the patient’s infection site, infection severity, pathogenic bacteria type, susceptibility results, and pathophysiological characteristics of the patient; (II) routine systemic antibiotics for infection prevention are not recommended in all immunocompromised patients; (III) establish a routine for antimicrobial application consultation; (IV) special antimicrobials include cefepime, imipenem, meropenem, vancomycin, notovancomycin, linezolid, intravenous azithromycin, caspofungin, itraconazole, voriconazole, and amphotericin B liposomes; (V) clinicians should use special antimicrobials based on pathogenic bacteria and susceptibility results; and (VI) if there is no etiological basis, clinicians should consult with infectious disease specialists. Clinicians may use special antibiotics with permission from infectious disease specialists.

Diagnosis of LRTI

Combined with the diagnostic criteria of hospital infection in China (11), LRTI can be diagnosed if it meets one of the following two conditions: (I) the patient has cough, viscous sputum, moist rales in the lungs, and one of the following conditions: (i) fever; (ii) the total number of white blood cells and/or the proportion of neutrophils increased; (iii) X-ray/chest computed tomography (CT) showed inflammatory infiltrative lesions in the lungs. (II) Pathological changes: for patients with chronic airway diseases (such as chronic bronchitis with or without obstructive emphysema, asthma, bronchiectasis), secondary acute infection in stable period, and pathogenic changes or X-ray chest film shows obvious changes or new lesions compared with admission. Etiological indicators are not used as a basis for diagnosis in this study.

Data collection procedure

The investigators collected the data of children who met the inclusion criteria. Since the literature review of infection after CAR-T cell therapy tends to occur 0–30 days after cell infusion (3-7), we observed and recorded the occurrence of LRTI within 30 days after CAR-T cell infusion. Other data as potential predictors were respectively collected on the day of admission, the day of CAR-T cell infusion (0 day),
and the 7^th, 14^th, 21^st, and 28^th days after CAR-T cell infusion. The source of data was electronic medical records. Collection stopped when LRTI occurred. Outcome measures were judged independently by physicians who were unaware of predictors. The collection of predictive indicators was done by the investigators, who were unaware of the outcome indicators.

Statistical analysis

This study was a complete-case analysis. SPSS 19 software and R Studio 4.4.1 software were used for statistical analysis, and the statistical data were statistically described by frequency and percentage. The normal distribution test was carried out firstly; the data with normal distribution were statistically described by two independent samples t-test; and the mean ± standard deviation was used to statistically describe the data. Non-conforming to the normal distribution of data were performed by non-parametric tests (Mann-Whitney U test) and statistically described with median and quartile median (P25, P75). Pearson Chi-squared (χ²) test was used for classification data, and rate (%) was used for statistical description. All statistical analyses were carried out at α=0.05, P<0.05 for statistical significance.

Collinearity test was performed on the factors with statistical significance in univariate analysis, and the variables with a variance inflation factor (VIF) less than 10 were included in the multivariate logistic regression analysis.

To identify independent risk factors for LRTI, variables with a P<0.05 in the univariable analysis were included in the multivariable logistic regression analysis. The results were presented as adjusted odds ratios (ORs) with their 95% confidence intervals (CIs). A two-sided P<0.05 was considered statistically significant. The discriminative ability of the model was evaluated by the area under the receiver operating characteristic (ROC) curve. The R project for statistical computing was used to plot the nomogram of the predictive model.

Results

Clinical characteristics

A total of 285 samples met the inclusion criteria. Nine patients did not receive CAR-T treatment due to changes in their condition, and 11 patients developed LRTI prior to CAR-T cell infusion. The final sample size in this study was 265 (Figure 1).

Figure 1 Inclusion and exclusion process. CAR-T, chimeric antigen receptor T; LRTI, lower respiratory tract infection.

Within 0–30 days after CAR-T cell infusion, 39 patients (14.7%) developed LRTI, while 226 patients (85.3%) did not develop LRTI. LRTI occurred between the 3^rd and 29^th day after CAR-T cell infusion, 28 cases (71.8%) occurred within 0–14 days, and 11 cases (28.2%) occurred within 15–30 days.

Compared to the non-LRTI group, the LRTI group included a higher proportion of individuals who showed a high level of MRD prior to CAR-T cell infusion, received more chemotherapy than fludarabine and cyclophosphamide prior to CAR-T cell infusion, received allogeneic CAR-T cell infusion, showed a lower level of WBC on infusion day (0 day), showed a lower level of PLT on infusion day (0 day), showed a lower level of ANC on infusion day (0 day), suffered more severe CRS and received dexamethasone after CAR-T cell therapy (Table 1).

Screening for predictors of LRTI and constructing a predictive model

Chemotherapy, allogeneic CAR-T cell, CRS (max), dexamethasone, WBC, PLT, ANC, MRD, which had statistical significance in univariate analysis, were used as multivariate logistic regression variables, and the outcome variable was LRTI (Table 2). According to Common Terminology Criteria for Adverse Events (CTCAE) v6.0 (published July 22, 2025), WBC <2.0×10⁹/L indicates grade 3 or higher leukopenia; PLT <50×10⁹/L indicates grade 3 or higher thrombocytopenia; ANC <1.0×10⁹/L indicates grade 2 and higher neutropenia. In clinical work, doctors and nurses grade thrombocytopenia, leukopenia, and neutropenia to facilitate better management of hematological toxicity. Therefore, in this study, we converted these continuous variables into categorical variables.

Collinearity test results showed that VIFs of all variables were less than 5, and tolerances (TOLs) were greater than 0.2, indicating that there was no collinearity among all variables.

Multivariate logistic regression analysis showed that PLT <50×10⁹/L (X1), MRD >20% (X2), dexamethasone >10 mg (X3), and allogeneic CAR-T (X4) entered the regression equation model at α=0.05 test level, and all of them were positively correlated with the risk of LRTI. The regression equation was y (LRTI incidence) = 1.027 × X1 + 1.079 × X2 + 1.187 × X3 + 1.096 × X4. PLT <50×10⁹/L was an independent risk factor for LRTI development (OR =2.792). Children with PLT <50×10⁹/L on the day of CAR-T cell infusion (0 day) had a 2.792-fold risk of LRTI development compared with other children. MRD >20% was an independent risk factor for LRTI (OR =2.940). If MRD >20% occurred in the MRD test performed from this admission to CAR-T cell infusion, the risk of LRTI after CAR-T infusion was 2.940 times higher than that of other children. Dexamethasone doses greater than 10 mg were found to be an independent risk factor for the development of LRTI (OR =3.277). The risk of developing LRTI was 3.277 times higher in children who received dexamethasone greater than 10 mg after CAR-T cell infusion. Allogeneic CAR-T cell was an independent risk factor for the development of LRTI (OR =2.993). The risk of developing LRTI was 2.993 times higher in patients infused with CAR-T cells from allogeneic donors than in patients infused with autologous CAR-T cells. The results are shown in Table 3.

Evaluation of the predictive model

Hosmer-Lemeshow test showed that χ² was 2.674 (P=0.95), indicating that the predicted risk of LRTI was consistent with the actual risk. The area under the ROC curve was 0.781 (P>0.001), >0.7, indicating that the model had good discrimination performance. The maximum Youden index was 0.468, and the cut-off value was 0.139. The sensitivity and specificity of the corresponding model were 76.9% and 69.9%, respectively. ROC curves are shown in Figure 2.

Figure 2 ROC curve for the predictive model. ROC, receiver operating characteristic.

Construction of nomograms for the predictive model

The independent risk factors obtained by multivariate analysis were constructed by R4.4.1, and the corresponding scores on the “score” axis of the nomogram were selected according to the classification of risk factors. The scores of each risk factor are summed to obtain the total score, and a vertical line is drawn downward at the position of the total score. The value intersected with the “probability of occurrence” axis is the probability of developing LRTI in children with leukemia treated with CAR-T cells. The nomogram is shown in Figure 3.

Figure 3 Nomogram of the predictive model. CAR-T, chimeric antigen receptor T; MRD, minimal residual disease; PLT, platelet count.

Discussion

Key findings

In this study, the incidence of LRTI within 0–30 days after CAR-T cell infusion was 14.7%. LRTI occurred on days 3–29 after cell infusion, of which 28 cases (71.8%) occurred within 0–14 days and 11 cases (28.2%) occurred on days 14–30. According to Park’s infection study in B-lineage acute lymphoblastic leukemia (B-ALL) patients within the first 30 days after CAR-T treatment (3), 22 patients out of 53 patients experienced 26 infection events, of which five events were lung infections, accounting for 19.2% of infection events. The incidence of LRTI in this study was 14.7%, slightly lower than Park’s study results. This study found that the incidence of LRTI within 0–14 days after CAR-T cell infusion was much higher than that within 14–30 days. Hill found that 80% of infections within 28 days after CAR-T cell infusion occurred within the first 10 days (4). The results of this study were consistent with those of Hill.

This study found that PLT <50×10⁹/L was an independent risk factor for LRTI development. Thrombocytopenia has a high incidence during the treatment of children with leukemia. According to the classification standard of tumor drug-related thrombocytopenia (12), PLT <50×10⁹/L is grade 3 or above thrombocytopenia. Platelets are primarily responsible for hemostasis. However, recent studies have shown that platelets are also involved in immune responses, capable of detecting pathogens and interacting with and killing them (13). Platelets can act as circulating sentinels by expressing Toll-like receptors (TLRs) that bind pathogens, effectively killing pathogens or presenting them to other immune cells. In addition, activated platelets secrete and express a number of pro-inflammatory and anti-inflammatory molecules that attract and trap white blood cells and direct them to inflamed tissues. Platelets can also directly affect adaptive immune response by secreting CD40 and CD40L molecules. Platelets and megakaryocytes can also ingest, process, and present exogenous and autoantigenic antigens to CD8⁺ T cells, thereby giving them the ability to directly alter adaptive immune responses (14). A study of thrombocytopenia after cardiopulmonary bypass found that postoperative thrombocytopenia was associated with an increased risk of postoperative infection (15). And a study for the association of platelet decreases following continuous renal replacement therapy initiation and increased rates of secondary Infections also showed that thrombocytopenia may increase the risk of secondary infections (16). Lymphodepleting chemotherapy given to patients prior to CAR-T cell infusion can help enhance CAR-T efficacy, but it results in bone marrow suppression and persistent cytopenia (17). Although there is no direct evidence linking thrombocytopenia to LRTI, we propose the possibility based on the results of this study that low platelets may increase the risk of infections after CAR-T cell infusion, including LRTI. This suggests that researchers should pay attention to PLT changes during CAR-T cell therapy.

In this study, MRD >20% was found to be an independent risk factor for LRTI. MRD is defined as measurable leukemia in a sample that is devoid of leukemia cells by morphological assessment with a light microscope (18). Accurate identification of MRD in treatment for acute lymphoblastic leukemia is the most important independent prognostic biomarker for predicting response to combination chemotherapy. MRD-based stratification maximizes treatment effectiveness while minimizing adverse reactions (19). MRD can be used to assess the tumor burden of children during CAR-T therapy. High MRD indicates that the tumor burden of children is relatively high. Such children need to reduce the tumor burden by chemotherapy and radiotherapy before receiving CAR-T cell therapy. Previous studies have found that multi-line anti-tumor therapy in the early stage may increase the infection risk of CAR-T cell therapy patients (4,20). It is suggested that investigators might need to take more active LRTI preventive measures for children with higher MRD during CAR-T cell therapy.

In this study, dexamethasone doses above than 10 mg were found to be an independent risk factor for the development of LRTI. The dexamethasone was given after the infusion of CAR-T cells and before the diagnosis of LRTI. It acted as an immune suppression trigger. The dose of dexamethasone administered by doctors is 0.1–0.2 mg/kg. Some children with severe CRS or ICANS symptoms may receive multiple doses of dexamethasone. Dexamethasone-mediated T cell suppression reduces naive T cell proliferation and differentiation by attenuating CD28 costimulatory pathways (21). In CAR-T therapy, dexamethasone is used to relieve severe CRS and ICANS (22). Previous CAR-T-related studies have identified systemic corticosteroid use as a risk factor for an increased risk of infection (23), which is consistent with the results of this study. These suggest that children using dexamethasone at doses greater than 10 mg during CAR-T cell therapy may need to take more aggressive LRTI prophylaxis. Nurses will conduct closer vital sign monitoring and early symptom assessment for high-risk patients, and timely identify early symptoms of respiratory tract infection, such as fever, cough, expectoration, and increased respiratory rate. Nurses inform doctors of abnormal vital signs and early symptoms, and complete appropriate etiological and imaging tests. Other medications may be used as an alternative to the use of dexamethasone. A study showed ruxolitinib was active and well-tolerated in steroid-refractory and even life-threatening CRS (24).

This study found that allogeneic CAR-T cells were an independent risk factor for the development of LRTI. Previous studies did not identify this factor. In this study, most of the patients who received allogeneic CAR-T cell therapy were patients after hematopoietic stem cell transplantation or those whose own lymphocytes were insufficient to produce CAR-T cells (25), suggesting that patients with long-term immunosuppression or a poor autoimmune base may lead to a high risk of LRTI infection (26,27). This result suggests that more aggressive LRTI prophylaxis might be needed in children with allogeneic CAR-T cell transfusion.

Strengths and limitations

The predictive model established in this study has good consistency and discrimination, and the nomogram provides visual calculation of LRTI occurrence risk probability, which can prompt medical staff to closely monitor children with high risk of LRTI occurrence and take more proactive preventive care measures to reduce the incidence of LRTI. In the future, we plan to integrate predictive models with hospital information systems (HIS) to predict a patient’s risk of developing LRTIs from the day the patient infused with CAR-T cells. Risk factors of PLT and MRD can be automatically obtained from laboratory results. CAR-T cell source (autologous or allogeneic) and dexamethasone dose can be automatically obtained from the order information. The results calculated by the electronic system will indicate whether the patient is at high risk for LRTI.

As this study was a single-center retrospective study, only 39 patients with positive outcomes were finally included, with a total sample size of 265 patients. On the premise of ensuring stable estimation of model parameters, we did not split the dataset for internal verification. This means that the actual generalization ability of the model may be lower than the results reported in this article. For further external validation, more samples are needed. In the future, multi-center studies may be considered for external validation of the model. In addition, the factors included in the study are objective factors such as demography, disease, treatment, and test indicators. In the future, we can consider symptomatic factors to find more possible risk factors and build a more accurate prediction model. In this study, we find the source of CAR-T cells (autologous vs. allogeneic CAR-T cells) is an influencing factor for the occurrence of LRTI, which is a new finding. However, due to the small number of patients receiving allogeneic CAR-T cell therapy, it may be necessary to accumulate a larger sample size. In future studies, we will include larger sample sizes and analyze the validity of the model for both patient groups.

Conclusions

This study explored the incidence of LRTI within 0–30 days after CAR-T cell infusion in children with leukemia, and screened out independent risk factors for LRTI. The prediction model established can provide a certain degree of reference for medical staff to assess the risk of LRTI in children with leukemia treated with CAR-T cells, which may play a positive role in reducing the occurrence of LRTI.

Acknowledgments

None.

Footnote

Reporting Checklist: The authors have completed the TRIPOD reporting checklist. Available at https://tp.amegroups.com/article/view/10.21037/tp-2025-503/rc

Data Sharing Statement: Available at https://tp.amegroups.com/article/view/10.21037/tp-2025-503/dss

Peer Review File: Available at https://tp.amegroups.com/article/view/10.21037/tp-2025-503/prf

Funding: This study was supported by the Huhang Nursing Research fund of Shanghai Anticancer Association (No. SACA-HH202302), the Management Research Project of Shanghai Shenkang Hospital Development Center (No. 2024SKMR-29), and the Clinical Research Plan of SHDC (No. SHDC2023CRS010).

Conflicts of Interest: All authors have completed the ICMJE uniform disclosure form (available at https://tp.amegroups.com/article/view/10.21037/tp-2025-503/coif). The authors have no conflicts of interest to declare.

Ethical Statement: The authors are accountable for all aspects of the work in ensuring that questions related to the accuracy or integrity of any part of the work are appropriately investigated and resolved. This study was conducted in accordance with the Declaration of Helsinki and its subsequent amendments. The study was approved by the Ethics Committee of Shanghai Children’s Medical Center Affiliated to Shanghai Jiao Tong University (No. SCMCIRB-K202318-1). Written informed consent was obtained from the voluntary participating guardians.

Open Access Statement: This is an Open Access article distributed in accordance with the Creative Commons Attribution-NonCommercial-NoDerivs 4.0 International License (CC BY-NC-ND 4.0), which permits the non-commercial replication and distribution of the article with the strict proviso that no changes or edits are made and the original work is properly cited (including links to both the formal publication through the relevant DOI and the license). See: https://creativecommons.org/licenses/by-nc-nd/4.0/.

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